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A combined forecasting approach with model self-adjustment for renewable generations and energy loads in smart community

Author

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  • Li, Yong
  • Wen, Zhe
  • Cao, Yijia
  • Tan, Yi
  • Sidorov, Denis
  • Panasetsky, Daniil

Abstract

The short-term forecasting of wind power, photovoltaic (PV) generation and loads is important for the secure and economical dispatching of smart community with smart grid. Considering the smart community has plenty of distributed generations, here, a concept of net load is defined as the active power difference between renewable generations (wind and PV power) and loads. Then, a combined forecasting approach, which enables to build a real-time forecasting model with parameters self-adjustment, is proposed for the forecasting of the net load in smart community. Compared with the traditional forecasting methods such as support vector machine (SVM), the proposed approach can wavily optimize the parameters of the forecasting model. Besides, an optimal method named Grid-GA searching is developed to reduce the computation time during the forecasting. Therefore, it can improve the forecasting accuracy even if there is a great of uncertainty component in wind power, PV generation and loads. Detailed case studies give a contrastive analysis of the traditional and the proposed forecasting approach. The results show that the proposed approach has the capability of self-adaption on the fluctuations of wind and PV power, and can effectively improve the forecasting accuracy and efficiency.

Suggested Citation

  • Li, Yong & Wen, Zhe & Cao, Yijia & Tan, Yi & Sidorov, Denis & Panasetsky, Daniil, 2017. "A combined forecasting approach with model self-adjustment for renewable generations and energy loads in smart community," Energy, Elsevier, vol. 129(C), pages 216-227.
    • Handle: RePEc:eee:energy:v:129:y:2017:i:c:p:216-227
    DOI: 10.1016/j.energy.2017.04.032
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    Cited by:

    1. Meng, LingYan & Li, Jinshi, 2023. "Efficient natural resource rents and carbon taxes in BRICS green growth," Resources Policy, Elsevier, vol. 86(PA).
    2. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
    3. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
    4. Wu, Jing & Xiao, Jian, 2022. "Development path based on the equalization of public services under the management mode of the Internet of Things," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    5. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
    6. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    7. Mauree, Dasaraden & Naboni, Emanuele & Coccolo, Silvia & Perera, A.T.D. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 733-746.
    8. Maolin Cheng & Jiano Li & Yun Liu & Bin Liu, 2020. "Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    9. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    10. Fang Liu & Ranran Li & Aliona Dreglea, 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model," Energies, MDPI, vol. 12(18), pages 1-16, September.

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